motors                 package:MASS                 R Documentation

_A_c_c_e_l_e_r_a_t_e_d _L_i_f_e _T_e_s_t_i_n_g _o_f _M_o_t_o_r_e_t_t_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     The 'motors' data frame has 40 rows and 3 columns.  It describes
     an accelerated life test at each of four temperatures of 10
     motorettes, and has rather discrete times.

_U_s_a_g_e:

     data(motors)

_F_o_r_m_a_t:

     This data frame contains the following columns:

     '_t_e_m_p' the temperature (degrees C) of the test

     '_t_i_m_e' the time in hours to failure or censoring at 8064 hours (=
          336 days).

     '_c_e_n_s' an indicator variable for death

_S_o_u_r_c_e:

     Kalbfleisch, J. D. and Prentice, R. L. (1980) _The Statistical
     Analysis of Failure Time Data._ New York: Wiley.

     taken from

     Nelson, W. D. and Hahn, G. J. (1972) Linear regression of a
     regression relationship from censored data. Part 1 - simple
     methods and their application. _Technometrics_, *14*, 247-276.

_R_e_f_e_r_e_n_c_e_s:

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

_E_x_a_m_p_l_e_s:

     library(survival)
     plot(survfit(Surv(time, cens) ~ factor(temp), motors), conf.int = FALSE)
     # fit Weibull model
     motor.wei <- survreg(Surv(time, cens) ~ temp, motors)
     summary(motor.wei)
     # and predict at 130C
     unlist(predict(motor.wei, data.frame(temp=130), se.fit = TRUE))

     motor.cox <- coxph(Surv(time, cens) ~ temp, motors)
     summary(motor.cox)
     # predict at temperature 200
     plot(survfit(motor.cox, newdata = data.frame(temp=200),
          conf.type = "log-log"))
     summary( survfit(motor.cox, newdata = data.frame(temp=130)) )

